Image-based size estimation of broccoli heads under varying degrees of occlusion

Blok, Pieter M.; Henten, E.J. van; Evert, F.K. van; Kootstra, Gert

doi:10.1016/j.biosystemseng.2021.06.001

Cited by 45 publications

(29 citation statements)

References 23 publications

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“…Image annotation has attracted widespread attention in the past few years due to the rapid growth of image data [9][10][11]. This method is used to analyze big data images and predict labels for the images [12].…”

Section: Introductionmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.

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Section: Introductionmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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“…But the proposed method was still unable to solve the occlusion problem between the blades, and the errors increased with growingover time. To solve the problem of occlusion, Blok et al ( 2021 ) estimated the size of field-grown broccoli heads based on RGB-Depth (RGB-D) images and applied the Occlusion Region-based Convolutional Neural Network (CNN) (Follmann et al, 2019 ). This method could predict the size of broccoli for different varieties under a high degree of occlusion, but the shape of broccoli itself is relatively regular, and the measured size was limited to the diameter.…”

Section: Introductionmentioning

confidence: 99%

Growth parameter acquisition and geometric point cloud completion of lettuce

Lou

Lu²,

Wang³

et al. 2022

Front. Plant Sci.

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The plant factory is a form of controlled environment agriculture (CEA) which is offers a promising solution to the problem of food security worldwide. Plant growth parameters need to be acquired for process control and yield estimation in plant factories. In this paper, we propose a fast and non-destructive framework for extracting growth parameters. Firstly, ToF camera (Microsoft Kinect V2) is used to obtain the point cloud from the top view, and then the lettuce point cloud is separated. According to the growth characteristics of lettuce, a geometric method is proposed to complete the incomplete lettuce point cloud. The treated point cloud has a high linear correlation with the actual plant height (R2 = 0.961), leaf area (R2 = 0.964), and fresh weight (R2 = 0.911) with a significant improvement compared to untreated point cloud. The result suggests our proposed point cloud completion method have has the potential to tackle the problem of obtaining the plant growth parameters from a single 3D view with occlusion.

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“…2D computer vision approaches with single-plant resolution monitor plant disease, health, and canopy area well [7], but struggle to estimate mass with high accuracy [8]: a critical component of growth modeling. 3D approaches include using RGB [9], Stereo [10], Time-of-Flight-based [11], [12], and a number of other specialized sensors to estimate plant properties such as mass, dimensions [13], and organ structure [14], [15]. In most cases, multiple viewpoints are required to accurately assess the 3D structure of a plant, with occlusions posing particular difficulty [13], [14].…”

Section: Introductionmentioning

confidence: 99%

“…3D approaches include using RGB [9], Stereo [10], Time-of-Flight-based [11], [12], and a number of other specialized sensors to estimate plant properties such as mass, dimensions [13], and organ structure [14], [15]. In most cases, multiple viewpoints are required to accurately assess the 3D structure of a plant, with occlusions posing particular difficulty [13], [14]. Existing approaches tend to focus on either high-throughput or high-accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion

Chen¹,

Harsh²,

Pradalier³

et al. 2022

Preprint

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We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, mediumthroughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structurefrom-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cabledriven parallel robot (CDPR) with the dexterity of a 4 degree-offreedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of overcanopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).

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Image-based size estimation of broccoli heads under varying degrees of occlusion

Cited by 45 publications

References 23 publications

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Growth parameter acquisition and geometric point cloud completion of lettuce

A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion

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